Datasets:
codah

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As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use this information to design challenging commonsense questions. Our experimental results show that CODAH questions present a complementary extension to the SWAG dataset, testing additional modes of common sense.\n", "citation": "@inproceedings{chen2019codah,\n title={CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense},\n author={Chen, Michael and D'Arcy, Mike and Liu, Alisa and Fernandez, Jared and Downey, Doug},\n booktitle={Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP},\n pages={63--69},\n year={2019}\n}\n", "homepage": "https://github.com/Websail-NU/CODAH", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "question_category": {"num_classes": 6, "names": ["Idioms", "Reference", "Polysemy", "Negation", "Quantitative", "Others"], "names_file": null, "id": null, "_type": "ClassLabel"}, "question_propmt": {"dtype": "string", "id": null, "_type": "Value"}, "candidate_answers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "correct_answer_idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "codah", "config_name": "fold_0", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 344912, "num_examples": 1665, "dataset_name": "codah"}, "validation": {"name": "validation", "num_bytes": 114211, "num_examples": 556, "dataset_name": "codah"}, "test": {"name": "test", "num_bytes": 112109, "num_examples": 555, "dataset_name": "codah"}}, "download_checksums": {"https://raw.githubusercontent.com/Websail-NU/CODAH/master/data/cv_split/fold_0/train.tsv": {"num_bytes": 293278, "checksum": "790edcdfcba7d07b66ae2f3074c9ed17239b1f0865962f1bea0e0768207f0d43"}, "https://raw.githubusercontent.com/Websail-NU/CODAH/master/data/cv_split/fold_0/dev.tsv": {"num_bytes": 96963, "checksum": "96c69a4240e6b0ef1f2364aeaa9d549e1b36188814f63d52bbc678a57b855923"}, "https://raw.githubusercontent.com/Websail-NU/CODAH/master/data/cv_split/fold_0/test.tsv": {"num_bytes": 94889, "checksum": "2065089015df59d7cf0328bdd32a9e2088ff66ab9639d926acf26a3b0203454e"}}, "download_size": 485130, "post_processing_size": null, "dataset_size": 571232, "size_in_bytes": 1056362}, "fold_1": {"description": "The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use this information to design challenging commonsense questions. Our experimental results show that CODAH questions present a complementary extension to the SWAG dataset, testing additional modes of common sense.\n", "citation": "@inproceedings{chen2019codah,\n title={CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense},\n author={Chen, Michael and D'Arcy, Mike and Liu, Alisa and Fernandez, Jared and Downey, Doug},\n booktitle={Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP},\n pages={63--69},\n year={2019}\n}\n", "homepage": "https://github.com/Websail-NU/CODAH", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "question_category": {"num_classes": 6, "names": ["Idioms", "Reference", "Polysemy", "Negation", "Quantitative", "Others"], "names_file": null, "id": null, "_type": "ClassLabel"}, "question_propmt": {"dtype": "string", "id": null, "_type": "Value"}, "candidate_answers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "correct_answer_idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "codah", "config_name": "fold_1", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 340990, "num_examples": 1665, "dataset_name": "codah"}, "validation": {"name": "validation", "num_bytes": 114211, "num_examples": 556, "dataset_name": "codah"}, "test": {"name": "test", "num_bytes": 116031, "num_examples": 555, "dataset_name": "codah"}}, "download_checksums": {"https://raw.githubusercontent.com/Websail-NU/CODAH/master/data/cv_split/fold_1/train.tsv": {"num_bytes": 289356, "checksum": "99841c58cbef1afb8aff8bf4ee8827d0824c3e787ff55d9a365a39284edde7ec"}, "https://raw.githubusercontent.com/Websail-NU/CODAH/master/data/cv_split/fold_1/dev.tsv": {"num_bytes": 96963, "checksum": "96c69a4240e6b0ef1f2364aeaa9d549e1b36188814f63d52bbc678a57b855923"}, "https://raw.githubusercontent.com/Websail-NU/CODAH/master/data/cv_split/fold_1/test.tsv": {"num_bytes": 98811, "checksum": "0f63cf4b30a7bd01f44f466cfa072bd99156e9f9a9042ac659f87b11e9bdbebb"}}, "download_size": 485130, "post_processing_size": null, "dataset_size": 571232, "size_in_bytes": 1056362}, "fold_2": {"description": "The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use this information to design challenging commonsense questions. Our experimental results show that CODAH questions present a complementary extension to the SWAG dataset, testing additional modes of common sense.\n", "citation": "@inproceedings{chen2019codah,\n title={CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense},\n author={Chen, Michael and D'Arcy, Mike and Liu, Alisa and Fernandez, Jared and Downey, Doug},\n booktitle={Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP},\n pages={63--69},\n year={2019}\n}\n", "homepage": "https://github.com/Websail-NU/CODAH", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "question_category": {"num_classes": 6, "names": ["Idioms", "Reference", "Polysemy", "Negation", "Quantitative", "Others"], "names_file": null, "id": null, "_type": "ClassLabel"}, "question_propmt": {"dtype": "string", "id": null, "_type": "Value"}, "candidate_answers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "correct_answer_idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "codah", "config_name": "fold_2", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 342293, "num_examples": 1665, "dataset_name": "codah"}, "validation": {"name": "validation", "num_bytes": 114211, "num_examples": 556, "dataset_name": "codah"}, "test": {"name": "test", "num_bytes": 114728, "num_examples": 555, "dataset_name": "codah"}}, "download_checksums": {"https://raw.githubusercontent.com/Websail-NU/CODAH/master/data/cv_split/fold_2/train.tsv": {"num_bytes": 290657, "checksum": "79ea621ba442cb8ebb64bf06179a038cfbccc875947a49fca1a487ce79bdc652"}, "https://raw.githubusercontent.com/Websail-NU/CODAH/master/data/cv_split/fold_2/dev.tsv": {"num_bytes": 96963, "checksum": "96c69a4240e6b0ef1f2364aeaa9d549e1b36188814f63d52bbc678a57b855923"}, "https://raw.githubusercontent.com/Websail-NU/CODAH/master/data/cv_split/fold_2/test.tsv": {"num_bytes": 97510, "checksum": "285996f708245858789a9a99740de07cb20c33b8fb071e772a047907727ed0ac"}}, "download_size": 485130, "post_processing_size": null, "dataset_size": 571232, "size_in_bytes": 1056362}, "fold_3": {"description": "The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. 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Our experimental results show that CODAH questions present a complementary extension to the SWAG dataset, testing additional modes of common sense.\n", "citation": "@inproceedings{chen2019codah,\n title={CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense},\n author={Chen, Michael and D'Arcy, Mike and Liu, Alisa and Fernandez, Jared and Downey, Doug},\n booktitle={Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP},\n pages={63--69},\n year={2019}\n}\n", "homepage": "https://github.com/Websail-NU/CODAH", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "question_category": {"num_classes": 6, "names": ["Idioms", "Reference", "Polysemy", "Negation", "Quantitative", "Others"], "names_file": null, "id": null, "_type": "ClassLabel"}, "question_propmt": {"dtype": "string", "id": null, "_type": "Value"}, "candidate_answers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "correct_answer_idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "codah", "config_name": "fold_3", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 342844, "num_examples": 1665, "dataset_name": "codah"}, "validation": {"name": "validation", "num_bytes": 114211, "num_examples": 556, "dataset_name": "codah"}, "test": {"name": "test", "num_bytes": 114177, "num_examples": 555, "dataset_name": "codah"}}, "download_checksums": {"https://raw.githubusercontent.com/Websail-NU/CODAH/master/data/cv_split/fold_3/train.tsv": {"num_bytes": 291210, "checksum": "27e6a6b55770bde68cedb97f8046af2fa469ca6082eb3ffc803ab61b39555bae"}, "https://raw.githubusercontent.com/Websail-NU/CODAH/master/data/cv_split/fold_3/dev.tsv": {"num_bytes": 96963, "checksum": "96c69a4240e6b0ef1f2364aeaa9d549e1b36188814f63d52bbc678a57b855923"}, "https://raw.githubusercontent.com/Websail-NU/CODAH/master/data/cv_split/fold_3/test.tsv": {"num_bytes": 96957, "checksum": "eb91713dd8aaca7bf7997168bd072ea0aa55c947c91e6f0869f48a2904a3c8fd"}}, "download_size": 485130, "post_processing_size": null, "dataset_size": 571232, "size_in_bytes": 1056362}, "fold_4": {"description": "The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. 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Our experimental results show that CODAH questions present a complementary extension to the SWAG dataset, testing additional modes of common sense.\n", "citation": "@inproceedings{chen2019codah,\n title={CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense},\n author={Chen, Michael and D'Arcy, Mike and Liu, Alisa and Fernandez, Jared and Downey, Doug},\n booktitle={Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP},\n pages={63--69},\n year={2019}\n}\n", "homepage": "https://github.com/Websail-NU/CODAH", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "question_category": {"num_classes": 6, "names": ["Idioms", "Reference", "Polysemy", "Negation", "Quantitative", "Others"], "names_file": null, "id": null, "_type": "ClassLabel"}, "question_propmt": {"dtype": "string", "id": null, "_type": "Value"}, "candidate_answers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "correct_answer_idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "codah", "config_name": "fold_4", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 342844, "num_examples": 1665, "dataset_name": "codah"}, "validation": {"name": "validation", "num_bytes": 114177, "num_examples": 555, "dataset_name": "codah"}, "test": {"name": "test", "num_bytes": 114211, "num_examples": 556, "dataset_name": "codah"}}, "download_checksums": {"https://raw.githubusercontent.com/Websail-NU/CODAH/master/data/cv_split/fold_4/train.tsv": {"num_bytes": 291210, "checksum": "27e6a6b55770bde68cedb97f8046af2fa469ca6082eb3ffc803ab61b39555bae"}, "https://raw.githubusercontent.com/Websail-NU/CODAH/master/data/cv_split/fold_4/dev.tsv": {"num_bytes": 96957, "checksum": "eb91713dd8aaca7bf7997168bd072ea0aa55c947c91e6f0869f48a2904a3c8fd"}, "https://raw.githubusercontent.com/Websail-NU/CODAH/master/data/cv_split/fold_4/test.tsv": {"num_bytes": 96963, "checksum": "96c69a4240e6b0ef1f2364aeaa9d549e1b36188814f63d52bbc678a57b855923"}}, "download_size": 485130, "post_processing_size": null, "dataset_size": 571232, "size_in_bytes": 1056362}}